PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

PPO‑HRAP introduces a hybrid regime‑aware policy that blends Proximal Policy Optimization with a volatility‑conditioned regime prior to balance upside participation and drawdown control in trading. The agent uses market and portfolio features, rewards that combine log return, VIX‑conditioned drawdown penalty, exposure deviation, and turnover cost, and outputs a blended action between the PPO actor and the regime‑derived target exposure. In backtests on SPY (2020‑2022) it achieved a 27.62% total return, 8.48% annualized return, and reduced maximum drawdown from 34.10% to 18.47%, while maintaining stable performance across multiple seeds and ranking first on total return and Sharpe ratio in single‑run cross‑asset tests on QQQ and DIA.

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• agents
• reinforcement-learning